Controlling a fabrication tool using support vector machine

ABSTRACT

A fabrication tool can be controlled using a support vector machine. A profile model of the structure is obtained. The profile model is defined by profile parameters that characterize the geometric shape of the structure. A set of values for the profile parameters is obtained. A set of simulated diffraction signals is generated using the set of values for the profile parameters, each simulated diffraction signal characterizing the behavior of light diffracted from the structure. The support vector machine is trained using the set of simulated diffraction signals as inputs to the support vector machine and the set of values for the profile parameters as expected outputs of the support vector machine. After the support vector machine has been trained, a fabrication process is performed using the fabrication tool to fabricate the structure on the wafer. A measured diffraction signal off the structure is obtained. The measured diffraction signal is inputted into the trained support vector machine. Values of profile parameters of the structure are obtained as an output from the trained support vector machine. One or more process parameters or equipment settings of the fabrication tool are adjusted based on the obtained values of the profile parameters.

The present application is a continuation of U.S. patent applicationSer. No. 11/787,025, filed on Apr. 12, 2007, issued as U.S. Pat. No.7,372,583, which is incorporated herein by reference in its entirety forall purposes.

BACKGROUND

1. Field

The present application generally relates to optical metrology ofstructures formed on semiconductor wafers, and, more particularly, tocontrolling a fabrication tool using a support vector machine.

2. Related Art

Optical metrology involves directing an incident beam at a structure,measuring the resulting diffracted beam, and analyzing the diffractedbeam to determine a feature of the structure. In semiconductormanufacturing, optical metrology is typically used for qualityassurance. For example, after fabricating a periodic grating inproximity to a semiconductor chip on a semiconductor wafer, an opticalmetrology system is used to determine the profile of the periodicgrating. By determining the profile of the periodic grating, the qualityof the fabrication process utilized to form the periodic grating, and byextension the semiconductor chip proximate the periodic grating, can beevaluated.

One conventional optical metrology system uses a diffraction modelingtechnique, such as rigorous coupled wave analysis (RCWA), to analyze thediffracted beam. More particularly, in the diffraction modelingtechnique, a model diffraction signal is calculated based, in part, onsolving Maxwell's equations. Calculating the model diffraction signalinvolves performing a large number of complex calculations, which can betime consuming and costly.

SUMMARY

In one exemplary embodiment, a fabrication tool can be controlled usinga support vector machine. A profile model of the structure is obtained.The profile model is defined by profile parameters that characterize thegeometric shape of the structure. A set of values for the profileparameters is obtained. A set of simulated diffraction signals isgenerated using the set of values for the profile parameters, eachsimulated diffraction signal characterizing the behavior of lightdiffracted from the structure. The support vector machine is trainedusing the set of simulated diffraction signals as inputs to the supportvector machine and the set of values for the profile parameters asexpected outputs of the support vector machine. After the support vectormachine has been trained, a fabrication process is performed using thefabrication tool to fabricate the structure on the wafer. A measureddiffraction signal off the structure is obtained. The measureddiffraction signal is inputted into the trained support vector machine.Values of profile parameters of the structure are obtained as an outputfrom the trained support vector machine. One or more process parametersor equipment settings of the fabrication tool are adjusted based on theobtained values of the profile parameters.

DESCRIPTION OF DRAWING FIGURES

The present invention can be best understood by reference to thefollowing description taken in conjunction with the accompanying drawingfigures, in which like parts may be referred to by like numerals:

FIG. 1 depicts an exemplary optical metrology system;

FIG. 2 depicts an exemplary process of examining a structure formed on asemiconductor wafer;

FIGS. 3A-3E depict exemplary profile models;

FIG. 4A depicts an exemplary one-dimension structure;

FIG. 4B depicts an exemplary two-dimension structure;

FIGS. 5A, 5B, and 5C depict exemplary profile models of two-dimensionstructures;

FIGS. 6A, 6B, and 6C depict graphs of accuracies of support vectormachines;

FIG. 7 depicts a comparison of results of using a support vector machineand a critical dimension-scanning electron microscope (CD-SEM);

FIG. 8 depicts another exemplary process of examining a structure on asemiconductor wafer;

FIG. 9 depicts an exemplary process of controlling a fabrication tool;and

FIG. 10 depicts a system of controlling a fabrication tool.

DETAILED DESCRIPTION

The following description sets forth numerous specific configurations,parameters, and the like. It should be recognized, however, that suchdescription is not intended as a limitation on the scope of the presentinvention, but is instead provided as a description of exemplaryembodiments.

With reference to FIG. 1, an optical metrology system 100 can be used toexamine and analyze a structure. For example, optical metrology system100 can be used to determine a feature of a periodic grating 102 formedon wafer 104. As described earlier, periodic grating 102 can be formedin test areas on wafer 104, such as adjacent to a device formed on wafer104. Alternatively, periodic grating 102 can be formed in an area of thedevice that does not interfere with the operation of the device or alongscribe lines on wafer 104.

As depicted in FIG. 1, optical metrology system 100 can include anoptical metrology device with a source 106 and a detector 112. Periodicgrating 102 is illuminated by an incident beam 108 from source 106. Inthe present exemplary embodiment, incident beam 108 is directed ontoperiodic grating 102 at an angle of incidence θ_(i) with respect tonormal {right arrow over (n)} of periodic grating 102 and an azimuthangle Φ (i.e., the angle between the plane of incidence beam 108 and thedirection of the periodicity of periodic grating 102). Diffracted beam110 leaves at an angle of θ_(d) with respect to normal {right arrow over(n)} and is received by detector 112. Detector 112 converts thediffracted beam 110 into a measured diffraction signal, which caninclude reflectance, tan (Ψ), cos (Δ), Fourier coefficients, and thelike. It should be recognized, however, that incident beam 108 can bedirected onto periodic grating 102 normal of periodic grating 102.

Optical metrology system 100 also includes a processing module 114 witha support vector machine 116. Processing module 114 is configured toreceive the measured diffraction signal and determine one or morefeatures of structure 102 using the measured diffraction signal andsupport vector machine 116.

With reference to FIG. 2, an exemplary process 200 of determining one ormore features of a structure formed on a semiconductor wafer isdepicted. In step 202, a profile model of the structure is obtained. Asdescribed in greater detail below, the profile model is defined byprofile parameters that characterize the geometric shape of thestructure.

For example, as depicted in FIG. 3A, profile model 300 is defined byprofile parameters h1 and w1 that define the height and width,respectively, of a structure. As depicted in FIGS. 3B to 3E, additionalshapes and features of the structure can be characterized by increasingthe number of profile parameters defining profile model 300. Forexample, as depicted in FIG. 3B, profile model 300 can be defined byprofile parameters h1, w1, and w2 that height, bottom width, and topwidth, respectively, of the structure. Note that the profile parameterw1 or w2 of profile model 300 can be referred to as the bottom criticaldimension (CD) and top CD, respectively. It should be recognized thatvarious types of profile parameters can be used to define profile model300, including angle of incident (AOI), pitch, n & k, hardwareparameters (e.g., polarizer angle), and the like.

The term “one-dimension structure” is used herein to refer to astructure having a profile that varies in one dimension. For example,FIG. 4A depicts a periodic grating having a profile that varies in onedimension (i.e., the x-direction). The profile of the periodic gratingdepicted in FIG. 4A varies in the z-direction as a function of thex-direction. However, the profile of the periodic grating depicted inFIG. 4A is assumed to be substantially uniform or continuous in they-direction.

The term “two-dimension structure” is used herein to refer to astructure having a profile that varies in two-dimensions. For example,FIG. 4B depicts a periodic grating having a profile that varies in twodimensions (i.e., the x-direction and the y-direction). The profile ofthe periodic grating depicted in FIG. 4B varies in the z-direction.

FIG. 5A depicts a top-view of exemplary orthogonal grid of unit cells ofa two-dimension repeating structure. A hypothetical grid of lines issuperimposed on the top-view of the repeating structure where the linesof the grid are drawn along the direction of periodicity. Thehypothetical grid of lines forms areas referred to as unit cells. Theunit cells may be arranged in an orthogonal or non-orthogonalconfiguration. Two-dimension repeating structures may comprise featuressuch as repeating posts, contact holes, vias, islands, or combinationsof two or more shapes within a unit cell. Furthermore, the features mayhave a variety of shapes and may be concave or convex features or acombination of concave and convex features. Referring to FIG. 5A, therepeating structure 500 comprises unit cells with holes arranged in anorthogonal manner. Unit cell 502 includes all the features andcomponents inside the unit cell 502, primarily comprising a hole 504substantially in the center of the unit cell 502.

FIG. 5B depicts a top-view of a two-dimension repeating structure. Unitcell 510 includes a concave elliptical hole. FIG. 5B shows a unit cell510 with a feature 520 that comprises an elliptical hole wherein thedimensions become progressively smaller until the bottom of the hole.Profile parameters used to characterize the structure includes theX-pitch 510 and the Y-pitch 514. In addition, the major axis of theellipse 516 that represents the top of the feature 520 and the majoraxis of the ellipse 518 that represents the bottom of the feature 520may be used to characterize the feature 520. Furthermore, anyintermediate major axis between the top and bottom of the feature mayalso be used as well as any minor axis of the top, intermediate, orbottom ellipse, (not shown).

FIG. 5C is an exemplary technique for characterizing the top-view of atwo-dimension repeating structure. A unit cell 530 of a repeatingstructure is a feature 532, an island with a peanut-shape viewed fromthe top. One modeling approach includes approximating the feature 532with a variable number or combinations of ellipses and polygons. Assumefurther that after analyzing the variability of the top-view shape ofthe feature 522, it was determined that two ellipses, Ellipsoid 1 andEllipsoid 2, and two polygons, Polygon 1 and Polygon 2 were found tofully characterize feature 532. In turn, parameters needed tocharacterize the two ellipses and two polygons comprise nine parametersas follows: T1 and T2 for Ellipsoid 1; T3, T4, and θ₁ for Polygon 1; T4,T5, and θ₂ for Polygon 2; T6 and T7 for Ellipsoid 2. Many othercombinations of shapes could be used to characterize the top-view of thefeature 532 in unit cell 530. For a detailed description of modelingtwo-dimension repeating structures, refer to U.S. patent applicationSer. No. 11/061,303, OPTICAL METROLOGY OPTIMIZATION FOR REPETITIVESTRUCTURES, by Vuong, et al., filed on Apr. 27, 2004, and isincorporated in its entirety herein by reference.

In one embodiment, correlations between profile parameters aredetermined. The profile parameters used to define the profile model areselected based on the determined correlations. In particular, theprofile parameters having correlations below a desired amount ofcorrelation are selected. Multivariate analysis can be used to determinethe correlations of profile parameters. Multivariate analysis caninclude a linear analysis or a nonlinear analysis. Additionally,multivariate analysis can include Principal Components Analysis (PCA),Independent Component Analysis, Cross Correlation Analysis, LinearApproximation Analysis, and the like. For a detailed description of amethod of determining correlations of multiple profile parameters, referto U.S. patent application Ser. No. 11/349,773, TRANSFORMING METROLOGYDATA FROM A SEMICONDUCTOR TREATMENT SYSTEM USING MULTIVARIATE ANALYSIS,by Vuong, et al., filed on May 8, 2006, and is incorporated in itsentirety herein by reference.

In step 204, a set of values for the profile parameters is obtained. Thevalues for the profile parameters in the set can be determined eitherempirically or through experience. For example, if the top width (i.e.,top CD) of the structure to be examined is expected to vary within arange of values, then a number of different values within the range ofvalues is used as the set obtained in step 204. For example, assume topCD is expected to vary within a range of 30 nanometers, such as between80 nanometers and 110 nanometers. A number of different values of top CDwithin the range of 80 nanometers and 110 nanometers are used as the setof values for the profile parameters in step 204.

In step 206, a set of simulated diffraction signals is generated usingthe set of values for the profile parameters. Each simulated diffractionsignal characterizing the behavior of light diffracted from thestructure. In one exemplary embodiment, the simulated diffraction signalcan be generated by calculating the simulated diffraction signal using anumerical analysis technique, such as rigorous coupled-wave analysis,with the profile parameters as inputs. In another exemplary embodiment,the simulated diffraction signal can be generated using a machinelearning algorithm, such as back-propagation, radial basis function,support vector, kernel regression, and the like. For more detail, seeU.S. Pat. No. 6,913,900, entitled GENERATION OF A LIBRARY OF PERIODICGRATING DIFFRACTION SIGNAL, by Niu, et al., issued on Sep. 13, 2005, andis incorporated in its entirety herein by reference.

In step 208, a support vector machine is trained using the set ofsimulated diffraction signals as inputs to the support vector machineand the set of values for the profile parameters as expected outputs ofthe support vector machine. Using the set of simulated diffractionsignals as inputs and the set of values for the profile parameters asexpected outputs, the support vector machine learns the function betweenthe two sets. More specifically, in one exemplary embodiment, thesupport vector machine uses a kernel function to transfer the set ofsimulated diffraction signals, which has a non-linear relationship withthe set of values for the profile parameters, to a feature space, whichhas a linear relationship to the set of values for the profileparameters. See, Lipo Wang, “Support Vector Machine—An introduction”Support Vector Machines: Theory and Applications, pages 1-45 (2005).

The accuracy of the support vector machine is typically improved byincreasing the number of simulated diffraction signals and values forthe profile parameters used in the training process. To increase thespeed of the training process, a sequential minimal optimization processcan be used. See, Platt, John C., “Fast Training of Support VectorMachines using Sequential Minimal Optimization,” Advances in kernelmethods: support vector learning, pages 185-208 (1999).

In one exemplary embodiment, after the training process, the supportvector machine can be tested using a test set of simulated diffractionsignals and a test set of values for profile parameters. Morespecifically, a test set of values for profile parameters is obtained.Preferably the values for the profile parameters in the test set aredifferent than the values used in the set used for training. However,the values used in the test set are within the range of values used fortraining. The test set of simulated diffraction signals is generatedusing the test set of values for profile parameters. The test set ofsimulated diffraction signals is inputted into the support vectormachine to generate an output set of values for profile parameters. Theoutput set is then compared to the test set of values for profileparameters to determine accuracy of the support vector machine.

FIGS. 6A and 6B are graphs depicting the accuracy of a support vectormachine trained using 2,000 training points (i.e., 2,000 values forprofile parameters in the set used for training and 2,000 simulateddiffraction signals in the set used for training) for a top CD range of30 nanometers. The accuracy depicted in FIGS. 6A and 6B is determined asthe difference between the expected value of top CD, which is the top CDvalue corresponding to the simulated diffraction signal used as theinput to the support vector machine, and the value of top CD generatedas an output of the support vector machine. In FIG. 6A, 500 test pointswere used to test the accuracy of the support vector machine. In FIG.6B, 2500 test points were used to test the accuracy of the supportvector machine.

If the accuracy of the support vector machine does not meet one or moreaccuracy criteria during the testing process, the support vector machinecan be retrained. In one exemplary embodiment, the support vectormachine can be retrained using one or more of the simulated diffractionsignals and values for profile parameters used in the testing process.

For example, with reference to FIG. 6B, several test points are depictedas exceeding 0.1 and −0.1 nanometers as normalized values. Thus, if theaccuracy criterion is that no test point can exceed 0.1 or −0.1nanometers, then the support vector machine is retrained. In oneexemplary embodiment, the values of profile parameters and the simulateddiffraction signals corresponding to the test points that exceed 0.1 or−0.1 nanometers are used in retraining the support vector machine. Itshould be recognized that various accuracy criteria can be used todetermine if the support vector machine is to be retrained. For example,a maximum number of test points exceeding 0.1 or −0.1 nanometers can beused as the accuracy criterion.

In one exemplary embodiment, the testing process can include introducinga noise signal into the simulated diffraction signals used for testing.For example, FIG. 6C depicts 500 test points with a noise level of 0.002(sigma) introduced into the simulated diffraction signals of the testset. The accuracy depicted in FIG. 6C is determined as the differencebetween the expected value of top CD, which is the top CD valuecorresponding to the simulated diffraction signal used as the input tothe support vector machine, and the value of top CD generated as anoutput of the support vector machine. The accuracy values in FIG. 6C arenormalized values.

After the support vector machine has been trained, tested, and/orretrained, one or more features of a structure can be determined usingthe support vector machine. In particular, in step 210, a measureddiffraction signal off the structure is obtained. After the supportvector machine has been trained, in step 212, the measured diffractionsignal is inputted into the trained support vector machine. In step 214,after step 212, values of profile parameters of the structure areobtained as an output from the trained support vector machine.

FIG. 7 depicts a graph comparing results obtained from using a supportvector machine and a CD-scanning electron microscope (CD-SEM) todetermine a feature of a structure (in this example, middle CD). Inparticular, the horizontal axis corresponds to values of middle CDdetermined using the support vector machine. The vertical axiscorresponds to values of middle CD determined using the CD-SEM. Thevalues of the middle CD are provided in nanometers and are notnormalized. As shown in FIG. 7, the results had an R² value of 0.9962.

In one exemplary embodiment, the values of profile parameters arenormalized values. More specifically, the values for the profileparameters obtained in step 204 are normalized. The support vectormachine is trained in step 208 using the normalized values for theprofile parameters. Thus, the values of profile parameters obtained asan output from the trained support vector machine in step 214 arenormalized values. In the present exemplary embodiment, the normalizedvalues obtained in step 214 are then de-normalized.

In one exemplary embodiment, the simulated diffraction signals aredefined using a standard set of signal parameters. The standard setincludes a reflectance parameter, which characterizes the change inintensity of light when reflected on the structure, and a polarizationparameter, which characterizes the change in polarization states oflight when reflected on the structure.

In the present exemplary embodiment, the reflectance parameter (R) ofthe standard set of signal parameters corresponds to an average of thesquare of the absolute value of the complex reflection coefficients ofthe light. The polarization parameter includes a first parameter (N)that characterizes half of the difference between the square of theabsolute value of the complex reflection coefficients normalized to R, asecond parameter (S) that characterizes the imaginary component of theinterference of the two complex reflection coefficients normalized to R,and a third parameter (C) that characterizes the real component of theinterference of the two complex reflection coefficients normalized to R.Thus, the standard set of signal parameters includes the parameters (R,NSC).

In the present exemplary embodiment, the simulated diffraction signalsgenerated in step 206 are defined using the standard set of signalparameters (R, NSC). The support vector machine is trained in step 208using simulated diffraction signals defined using the standard set ofsignal parameter (R, NSC). When the measured diffraction signal ismeasured using a reflectometer that only measures the change in theintensity of light, such as a spectrometer reflectometer, processingmodule 114 uses only the reflectance parameter of the standard set ofsignal parameters. When the measured diffraction signal is measuredusing an ellipsometer that measures both the change in the intensity oflight and polarization states of light, such as a rotating compensatorellipsometer (RCE), processing module 114 uses the reflectance parameterand the polarization parameter of the standard set of signal parameters.

With reference to FIG. 8, an exemplary process 800 of determining one ormore features of a structure formed on a semiconductor wafer isdepicted. In step 802, a profile model of the structure is obtained. Asdescribed above, the profile model is defined by profile parameters thatcharacterize the geometric shape of the structure. In step 804, atraining set of values for the profile parameters is obtained. In step806, a training set of simulated diffraction signals is generated usingthe training set of values for the profile parameters. As describedabove, each simulated diffraction signal characterizing the behavior oflight diffracted from the structure. In step 808, a support vectormachine is trained using the training set of values for the profileparameters as inputs to the support vector machine and the training setof simulated diffraction signals as expected outputs of the supportvector machine.

As described above, after the training process, the support vectormachine can be tested using a test set of simulated diffraction signalsand a test set of values for profile parameters. As also describedabove, if the accuracy of the support vector machine does not meet oneor more accuracy criteria during the testing process, the support vectormachine can be retrained.

After the support vector machine has been trained, tested, and/orretrained, one or more features of a structure can be determined usingthe support vector machine. In particular, in step 810, a measureddiffraction signal off the structure is obtained. In step 812, asimulated diffraction signal is generated using a set of values for theprofile parameters as inputs to the trained support vector machine. Instep 814, the measured diffraction signal is compared to the simulateddiffraction signal generated in 812. When the measured diffractionsignal and simulated diffraction signal match within one or morematching criteria, values of profile parameters of the structure aredetermined to be the set of values for the profile parameters used instep 812 to generate the simulated diffraction signal.

As described above, in one exemplary embodiment, the values of profileparameters are normalized values. As also described above, in oneexemplary embodiment, the simulated diffraction signals are definedusing a standard set of signal parameters (R, NSC).

In one exemplary embodiment, in step 812, a plurality of simulateddiffraction signals is generated using different sets of values for theprofile parameters as inputs to the trained support vector machine. Eachsimulated diffraction signal is associated with the set of values forthe profile parameters used to generate the simulated diffractionsignal. The plurality of simulated diffraction signals, the differentsets of values for the profile parameters, and the association betweeneach simulated diffraction signal with the set of values for the profileparameters used to generate the simulated diffraction signal are storedin a library 118 (FIG. 1).

In the present exemplary embodiment, when the measured diffractionsignal and the simulated diffraction signal do not match within one ormore matching criteria in step 814, the measured diffraction signal iscompared with another simulated diffraction signal from the library 118(FIG. 1) of simulated diffraction signals. When the measured diffractionsignal and the another simulated diffraction signal match within one ormore matching criteria, values of profile parameters of the structureare determined to be the set of values for the profile parametersassociated with the simulated diffraction signal in the library 118(FIG. 1).

In another exemplary embodiment, when the measured diffraction signaland the simulated diffraction signal do not match within one or morematching criteria in step 814, another simulated diffraction signal isgenerated using a set of different values for the profile parameters asinputs to the trained support vector machine. The measured diffractionsignal is compared to the another simulated diffraction signal. When themeasured diffraction signal and the another simulated diffraction signalmatch within one or more matching criteria, values of profile parametersof the structure are determined to be the set of different values forthe profile parameters used to generate the another simulateddiffraction signal.

FIG. 9 depicts an exemplary process of controlling a first fabricationtool used to fabricate a structure on a wafer. In step 902, a profilemodel of the structure is obtained. As described above, the profileparameters characterize the geometric shape of the structure. In step904, a set of values for the profile parameters is obtained. In step906, a set of simulated diffraction signals is generated using the setof values for the profile parameters. Each simulated diffraction signalcharacterizing the behavior of light diffracted from the structure. Instep 908, a support vector machine is trained using the set of simulateddiffraction signals as inputs to the support vector machine and the setof values for the profile parameters as expected outputs of the supportvector machine.

After the support vector machine has been trained, in step 910, afabrication process is performed using the first fabrication tool tofabricate the structure on the wafer. In step 912, after the structurehas been fabricated using the first fabrication tool, a measureddiffraction signal is obtained off the structure. In step 914, themeasured diffraction signal is inputted into the trained support vectormachine. In step 916, after step 914, values of profile parameters ofthe structure are obtained as an output from the trained support vectormachine. In step 918, one or more process parameters or equipmentsettings of the first fabrication tool are adjusted based on the valuesof the profile parameters obtained in step 916.

In one exemplary embodiment, one or more process parameters or equipmentsettings of a second fabrication tool are adjusted based on the one ormore values of the profile parameters obtained in step 916. The secondfabrication tool can process a wafer before or after the wafer isprocessed in the first fabrication tool.

For example, the first fabrication tool and the second fabrication toolcan be configured to perform photolithography, etch, thermal processing,metallization, implant, chemical vapor deposition, chemical mechanicalpolishing, and the like. In particular, the first fabrication tool canbe configured to perform a development step of a photolithographyprocess. The second fabrication tool can be configured to perform anexposure step, which is performed prior to the development step, of thephotolithography process. Alternatively, the first fabrication tool canbe configured to perform a development step of a photolithography step.The second fabrication tool can be configured to perform an etch step,which is performed subsequent to the development step, of thephotolithography process.

FIG. 10 depicts an exemplary system 1000 to control fabrication of astructure on a semiconductor wafer. System 1000 includes a firstfabrication tool 1002 and optical metrology system 1004. System 1000 canalso include a second fabrication tool 1006. Although second fabricationtool 1006 is depicted in FIG. 10 as being subsequent to firstfabrication tool 1002, it should be recognized that second fabricationtool 1006 can be located prior to first fabrication tool 1002 in system1000.

Optical metrology system 1004 includes an optical metrology device 1008,a support vector machine 1010, and processor 1012. Optical metrologydevice 1008 is configured to measure a diffraction signal off thestructure. Optical metrology device 1008 can be a reflectometer,ellipsometer, and the like.

As described above, support vector machine 1010 can be trained using aset of simulated diffraction signals as inputs to the support vectormachine and a set of values for the profile parameters as expectedoutputs of the support vector machine. The set of simulated diffractionsignals is generated using the set of values for the profile parameters,which characterize the geometric shape of the structure.

Processor 1012 is configured to input the measured diffraction signalinto support vector machine 1010. Processor 1012 is configured to obtainvalues of profile parameters of the structure as an output from supportvector machine 1010. Processor 1012 is also configured to adjust one ormore process parameters or equipment settings of first fabrication tool1002 based on the obtained values of the profile parameters. Asdescribed above, processor 1012 can be configured to also adjust one ormore process parameters or equipment settings of second fabrication tool1006 based on the obtained values of the profile parameters.

The foregoing descriptions of specific embodiments of the presentinvention have been presented for purposes of illustration anddescription. They are not intended to be exhaustive or to limit theinvention to the precise forms disclosed, and it should be understoodthat many modifications and variations are possible in light of theabove teaching.

1. A method of controlling a fabrication tool using a support vectormachine, the method comprising: a) performing a fabrication processusing a first fabrication tool to fabricate the structure on a wafer; b)obtaining a measured diffraction signal, wherein the measureddiffraction signal was measured off the structure using an opticalmetrology tool; c) inputting the measured diffraction signal into thesupport vector machine, wherein the support vector machine was trainedusing a set of simulated diffraction signals as inputs to the supportvector machine and a set of values for profile parameters as expectedoutputs of the support vector machine, wherein the set of simulateddiffraction signals was generated using the set of values for theprofile parameters, and wherein the profile parameters define a profilemodel that characterizes the geometric shape of the structure; d) afterc), obtaining values of profile parameters of the structure as an outputfrom the trained support vector machine; and e) after d), adjusting oneor more process parameters or equipment settings of the firstfabrication tool based on the values of the profile parameters obtainedin d).
 2. The method of claim 1, wherein the set of simulateddiffraction signals is generated using a standard set of signalparameters comprising a reflectance parameter, which characterizes thechange in intensity of light when reflected on the structure, and apolarization parameter, which characterizes the change in polarizationstates of light when reflected on the structure, wherein thepolarization parameter comprises: a first polarization parameter thatcharacterizes a difference between the square of the absolute value ofcomplex reflection coefficients average over depolarization effects, andnormalized to the reflectance parameter; a second polarization parameterthat characterizes an imaginary component of an interference of thecomplex reflection coefficients average over depolarization effects, andnormalized to the reflectance parameter; and a third polarizationparameter that characterizes a real component of an interference of thecomplex reflection coefficients average over depolarization effects, andnormalized to the reflectance parameter.
 3. The method of claim 1,further comprising: normalizing values of profile parameters, whereinthe set of simulated diffraction signals are generated using thenormalized values of profile parameters; and de-normalizing the valuesof profile parameter obtained in d).
 4. The method of claim 1, furthercomprising: determining correlations for profile parameters; andselecting the profile parameters used to define the profile model basedon the determined correlations.
 5. The method of claim 1, before c):obtaining a test set of simulated diffraction signals and a test set ofvalues for profile parameters; testing the support vector machine usingthe test set of simulated diffraction signals as inputs to the supportvector machine and the test set of values for profile parameters asexpected outputs of the support vector machine; and if one or moreaccuracy criteria are not met, retraining the support vector machineusing one or more of the simulated diffraction signals in the test setand one or more values for profile parameters in the test set.
 6. Themethod of claim 1, further comprising: adjusting one or more processparameters or equipment settings of a second fabrication tool based onthe values of the profile parameters determined in d).
 7. The method ofclaim 6, wherein the first fabrication tool processes the wafer prior tothe second fabrication tool.
 8. The method of claim 6, wherein the firstfabrication tool processes the wafer subsequent to the secondfabrication tool.
 9. A computer-readable storage medium containingcomputer executable instructions for causing a computer to control afabrication tool using a support vector machine, comprising instructionsfor: a) obtaining a measured diffraction signal that was measured off astructure on a wafer using an optical metrology tool, wherein thestructure was fabricated on the wafer by performing a fabricationprocess using a first fabrication tool; b) inputting the measureddiffraction signal into the support vector machine, wherein the supportvector machine was trained using a set of simulated diffraction signalsas inputs to the support vector machine and a set of values for profileparameters as expected outputs of the support vector machine, whereinthe set of simulated diffraction signals was generated using the set ofvalues for the profile parameters, and wherein the profile parametersdefine a profile model that characterizes the geometric shape of thestructure; c) after b), obtaining values of profile parameters of thestructure as an output from the trained support vector machine; and d)after c), adjusting one or more process parameters or equipment settingsof the first fabrication tool based on the values of the profileparameters obtained in c).
 10. The computer-readable storage medium ofclaim 9, wherein the set of simulated diffraction signals is generatedusing a standard set of signal parameters comprising a reflectanceparameter, which characterizes the change in intensity of light whenreflected on the structure, and a polarization parameter, whichcharacterizes the change in polarization states of light when reflectedon the structure, wherein the polarization parameter comprises: a firstpolarization parameter that characterizes a difference between thesquare of the absolute value of complex reflection coefficients averageover depolarization effects, and normalized to the reflectanceparameter; a second polarization parameter that characterizes animaginary component of an interference of the complex reflectioncoefficients average over depolarization effects, and normalized to thereflectance parameter; and a third polarization parameter thatcharacterizes a real component of an interference of the complexreflection coefficients average over depolarization effects, andnormalized to the reflectance parameter.
 11. The computer-readablestorage medium of claim 9, further comprising instructions for:normalizing values of profile parameters, wherein the set of simulateddiffraction signals are generated using the normalized values of profileparameters; and de-normalizing the values of profile parameter obtainedin c).
 12. The computer-readable storage medium of claim 9, furthercomprising instructions for: determining correlations for profileparameters; and selecting the profile parameters used to define theprofile model based on the determined correlations.
 13. Thecomputer-readable storage medium of claim 9, before b): obtaining a testset of simulated diffraction signals and a test set of values forprofile parameters; testing the support vector machine using the testset of simulated diffraction signals as inputs to the support vectormachine and the test set of values for profile parameters as expectedoutputs of the support vector machine; and if one or more accuracycriteria are not met, retraining the support vector machine using one ormore of the simulated diffraction signals in the test set and one ormore values for profile parameters in the test set.
 14. Thecomputer-readable storage medium of claim 9, further comprisinginstructions for: adjusting one or more process parameters or equipmentsettings of a second fabrication tool based on the values of the profileparameters determined in c).
 15. The computer-readable storage medium ofclaim 14, wherein the first fabrication tool processes the wafer priorto the second fabrication tool.
 16. The computer-readable storage mediumof claim 14, wherein the first fabrication tool processes the wafersubsequent to the second fabrication tool.
 17. A system to system tocontrol fabrication of a structure on a semiconductor wafer, comprising:a first fabrication tool configured to perform a fabrication process tofabricate the structure on the wafer; an optical metrology deviceconfigured to measure a diffraction signal off the structure on thewafer; and a processor configured to input the measured diffractionsignal into a support vector machine, obtain values of profileparameters of the structure as an output from the trained support vectormachine, and adjust one or more process parameters or equipment settingsof the first fabrication tool based on the obtained values of theprofile parameters, wherein the support vector machine was trained usinga set of simulated diffraction signals as inputs to the support vectormachine and a set of values for the profile parameters as expectedoutputs of the support vector machine, wherein the set of simulateddiffraction signals was generated using the set of values for theprofile parameters, and wherein the profile parameters characterize thegeometric shape of the structure.
 18. The system of claim 17, whereinthe set of simulated diffraction signals is generated using a standardset of signal parameters comprising a reflectance parameter, whichcharacterizes the change in intensity of light when reflected on thestructure, and a polarization parameter, which characterizes the changein polarization states of light when reflected on the structure, whereinthe polarization parameter comprises: a first polarization parameterthat characterizes a difference between the square of the absolute valueof complex reflection coefficients average over depolarization effects,and normalized to the reflectance parameter; a second polarizationparameter that characterizes an imaginary component of an interferenceof the complex reflection coefficients average over depolarizationeffects, and normalized to the reflectance parameter; and a thirdpolarization parameter that characterizes a real component of aninterference of the complex reflection coefficients average overdepolarization effects, and normalized to the reflectance parameter. 19.The system of claim 17, wherein the set of simulated diffraction signalsare generated using normalized values of profile parameters, and whereinthe processor is configured to de-normalize the values of profileparameters obtained as outputs from the support vector machine.
 20. Thesystem of claim 17, wherein the support vector machine is tested using atest set of simulated diffraction signals and a test set of values forprofile parameters, and wherein, if one or more accuracy criteria arenot met when the support vector machine is tested, the support vectormachine is retrained using one or more of the simulated diffractionsignals in the test set and one or more values for profile parameters inthe test set.
 21. The system of claim 17, further comprising: a secondfabrication tool configured to perform a fabrication process on thewafer, wherein the processor is configured to adjust one or more processparameters or equipment settings of the second fabrication tool based onthe obtained values of the profile parameters.
 22. The system of claim21, wherein the first fabrication tool processes the wafer prior to thesecond fabrication tool.
 23. The system of claim 21, wherein the firstfabrication tool processes the wafer subsequent to the secondfabrication tool.